An autonomous vehicle is a motorized vehicle that can operate without a human driver. An autonomous vehicle can include, for example, multiple sensor systems, such as a lidar sensor system, an imaging sensor system, and a radar sensor system, amongst others. The autonomous vehicle utilizes sensor signals output by the sensor systems to control its operation.
As the autonomous vehicle is operated, sensor signals from a sensor system can provide a substantive amount of data representative of the surroundings of the autonomous vehicle. Such data can include point cloud data representative of a region relative to the autonomous vehicle and, thus, can have a rich, complex structure. Further, the data can be formed from input signals from several sensor systems of the autonomous vehicle. Such a consolidation can render the data even more voluminous. According to an illustration, each point in three-dimensional (3D) space maintained in a point cloud data structure can have multiple channels (signals) such as, for example, position of a point in 3D space, point cloud distribution around the point (covariance), surface normal vector around the point, color channels, etc.
As such, storage of point cloud data typically includes compression of at least some of the point cloud data. Yet, conventional technologies to compress point cloud data typically rely on decompression of compressed point cloud data prior to operating mathematically on such data. Even as memory devices become smaller and cheaper, issues including memory fragmentation, virtualization, and other memory management techniques, can preclude from enlarging the data storage available in an autonomous vehicle. Therefore, much remains to be improved in conventional technologies to compress and manipulate data representative of the surroundings of an autonomous vehicle.